Federated Learning Aggregation Techniques and Multimodal Model Efficiency at the Edge

Abstract

Abstract: This report synthesises findings from 13 peer-reviewed papers addressing the following research question: What is the impact of different federated learning aggregation techniques on the inference efficiency and latency of multimodal models deployed across heterogeneous edge devices. Abstract The rapid evolution of large language models (LLMs) has driven a transformative shift in artificial intelligence (AI), reshaping both research paradigms and practical applications. Distinguished from their predecessors by unprecedented scale and advanced capabilities. 9 claims were extracted from source literature; 9 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 7.8/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research Question

What is the impact of different federated learning aggregation techniques on the inference efficiency and latency of multimodal models deployed across heterogeneous edge devices?

Verification Level

Descriptive public verification status only; aggregate claim counts are public, but individual claim records are not exposed here.

Quality Tier

Descriptive public triage only; this tier does not alter current publication or DOI behavior.

Quality Dimensions

Automated triage signals derived from public fields; not human peer review or independent validation.

Correction Record

Public corrections are additive records. Current status does not claim the synthesis is error-free.

Provenance